Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image

Abstract

After an earthquake, rapidly and accurately obtaining building damage information can help to effectively guide the implementation of the emergency rescue and can reduce disaster losses and casualties. Using a single post-earthquake fully-polarimetric synthetic aperture radar (PolSAR) image to interpret building damage information not only involves a guaranteed data source but is also easy and can be rapidly implemented. This paper is focused on rapid building earthquake damage detection in urban areas using post-earthquake PolSAR data. In PolSAR images, the undamaged buildings parallel to satellite flight pass are different from the collapsed buildings, but the undamaged buildings divergent to satellite flight pass are very similar to collapsed buildings because of their volume scattering characteristics. In this paper, the method of polarization orientation angle (POA) compensation is employed to increase the scattering power of buildings divergent to satellite flight pass, and then Wishart supervised classification is implemented on the PolSAR data after POA compensation. In addition, the two parameters of normalized difference of the dihedral component (NDDC) and pHHHV are proposed to improve the classification accuracy of the Wishart supervised classification, and both the undamaged buildings and collapsed buildings are determined. The study was carried out after the "4.14" Yushu earthquake in Yushu County, Qinghai province, China. The three damage levels are set for the urban area at the city block scale according to the values of the BBCR building damage index. The experimental results confirm that the scheme proposed in this paper can greatly improve the accuracy of the extraction of building damage information.

Full text

remote sensing
Article
Building Earthquake Damage Information Extraction
from a Single Post-Earthquake PolSAR Image
Wei Zhai 1,2,3,4, Huanfeng Shen 5, Chunlin Huang 2,4, * and Wansheng Pei 6
1Gansu Earthquake Administration, Lanzhou 730000, China; zwxzzzdsyhq@163.com
2Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and
Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and
Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
5School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;
shenhf@whu.edu.cn
6Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,
Lanzhou 730000, China; peiwansheng@126.com
*Correspondence: huangcl@lzb.ac.cn; Tel.: +86-0931-496-7975
Academic Editors: Zhenhong Li, Roberto Tomas, Zhong Lu and Prasad S. Thenkabail
Received: 25 November 2015; Accepted: 16 February 2016; Published: 25 February 2016
Abstract:
After an earthquake, rapidly and accurately obtaining building damage information can
help to effectively guide the implementation of the emergency rescue and can reduce disaster losses
and casualties. Using a single post-earthquake fully-polarimetric synthetic aperture radar (PolSAR)
image to interpret building damage information not only involves a guaranteed data source but is also
easy and can be rapidly implemented. This paper is focused on rapid building earthquake damage
detection in urban areas using post-earthquake PolSAR data. In PolSAR images, the undamaged
buildings parallel to satellite flight pass are different from the collapsed buildings, but the undamaged
buildings divergent to satellite flight pass are very similar to collapsed buildings because of their
volume scattering characteristics. In this paper, the method of polarization orientation angle (POA)
compensation is employed to increase the scattering power of buildings divergent to satellite flight
pass, and then Wishart supervised classification is implemented on the PolSAR data after POA
compensation. In addition, the two parameters of normalized difference of the dihedral component
(NDDC) and
ρHHHV
are proposed to improve the classification accuracy of the Wishart supervised
classification, and both the undamaged buildings and collapsed buildings are determined. The study
was carried out after the “4.14” Yushu earthquake in Yushu County, Qinghai province, China.
The three damage levels are set for the urban area at the city block scale according to the values of the
BBCR building damage index. The experimental results confirm that the scheme proposed in this
paper can greatly improve the accuracy of the extraction of building damage information.
Keywords: earthquake; buildings; damage assessment; PolSAR
1. Introduction
In recent years, earthquake disasters have become more frequent around the world. The earthquake
is one of the most dangerous natural disasters for human beings, and tens of billions of dollars in
property loss are caused by earthquakes every year. Unfortunately, earthquakes cannot be predicted
accurately at the current scientific level. Rapid and accurate damage assessment can help to reduce the
disaster loss and can provide decision support for the rescue and reconstruction efforts. Buildings are
Remote Sens. 2016,8, 171; doi:10.3390/rs8030171 www.mdpi.com/journal/remotesensing

Remote Sens. 2016,8, 171 2 of 14
the places where people live, and most of the casualties and economic losses in an earthquake are
caused by the damage to buildings [
1
]. Therefore, building damage assessment is one of the most
important parts of earthquake damage assessment.
The earthquake damage information obtained from a ground survey is the most accurate, but this
process is inefficient and takes a long period of time. Remote sensing, which is characterized by wide
coverage and speediness, is very suitable for areas with poor transport infrastructure and where there
is a risk of secondary disasters. Optical remote sensing images allow easy interpretation, but they are
susceptible to illumination variation [
2
]. Radar, with its strong penetrating ability, can operate day
and night, independent of weather conditions. As a result, radar has become an important means of
disaster assessment [
3
–
6
], but most of the studies of disaster assessment are based on multi-source [
7
,
8
]
or multi-temporal data [
9
–
12
]. However, obtaining the matched pre-earthquake data is difficult in
some situations, and the registration of the pre- and post-earthquake data is tricky and time-consuming.
Therefore, it is quicker and more convenient to undertake earthquake damage assessment using only
post-event single-temporal data.
Balz [
13
], Dell’Acqua et al. [
14
], and Polli et al. [
15
] have all evaluated building earthquake damage
using only post-earthquake single-polarization synthetic aperture radar (SAR) data. Nevertheless,
PolSAR (fully-polarimetric SAR) data record the scattering amplitude and phase of the HH
(horizontal/horizontal polarization), HV(horizontal/vertical polarization), VH(vertical/horizontal
polarization), and VV(vertical/vertical polarization)polarizations four ways for ground objects, and
can better assist with the understanding of scattering mechanisms [
16
] than single-polarization SAR
imagery. As a result, building damage assessment using PolSAR imagery is more accurate and more
reliable. Guo et al. [
17
] and Li et al. [
18
] introduced the parameter of
ρ
(circular polarization correlation
coefficient) and proposed the H-
α
-
ρ
method to extract the spatial distribution of collapsed buildings in
the Yushu urban area by using only a single post-earthquake SAR image. Subsequently,
Zhao et al. [19]
improved the H-
α
-
ρ
method and replaced the parameter of
ρ
with the normalized circular-pol correlation
coefficient (NCCC), and, at the same time, the homogeneity texture feature was employed to solve
the problem of collapsed buildings and buildings divergent to satellite flight pass being mixed with
each other. Shen et al. [
20
] extracted collapsed buildings based on feature template matching, using
13 polarimetric features. On account of the present research into building earthquake damage information
extraction being rather limited, this work aims to undertake some new research in this area.
A new scheme for earthquake damage assessment using only a single post-earthquake PolSAR
image is proposed in this study. This work explores the potential of using polarimetric information
to estimate earthquake damage for urban regions. In full PolSAR imagery, the scattering power of
collapsed buildings characterized by volume scattering is weak, and the undamaged buildings are
mainly characterized by double-bounce scattering, for which the scattering power is strong. However,
the buildings divergent to satellite flight pass, which are not parallel to the flight pass, with significant
cross-polarization backscattering, are similar to the collapsed buildings. This ambiguity between the
building types commonly results in overestimation of collapsed buildings in damage assessment.
The buildings divergent to satellite flight pass, rather than the buildings parallel to satellite flight
pass, rotate the polarization basis and induce a polarization orientation angle (POA) shift from zero [
21
].
Therefore, we first implement POA compensation for the original PolSAR data in order to solve the
scattering mechanism ambiguity between collapsed buildings and buildings divergent to satellite flight
pass. Wishart supervised classification is then performed on the PolSAR data after POA compensation
to extract the initial earthquake damage information. The parameters of the normalized difference of
the dihedral component (NDDC) and the HH-HV correlation coefficient (
ρHHHV
) are then introduced
to extract the buildings divergent to satellite flight pass, which are added to the undamaged buildings
generated from the Wishart supervised classification. The
ρHHHV
parameter is also used to improve
the vegetation classification result of the Wishart supervised classification. When the undamaged
buildings and collapsed buildings are acquired, the building collapse rate is quantized at the block

Remote Sens. 2016,8, 171 3 of 14
level by the building block collapse rate (BBCR). Finally, a map with three building damage levels for
the whole urban region is drawn according to the threshold value of the BBCR at the block scale.
2. Methodology
2.1. The Damage Assessment Procedures
There are five key procedures in the process flow of the damage estimation framework proposed
in this study, as shown in Figure 1. Firstly, according to Section 2.2, the method of POA compensation is
performed using the PolSAR data after preprocessing, and the new [T3] matrix after POA compensation
is obtained.
Remote Sens. 2016, 8, 171 3 of 14
2. Methodology
2.1. The Damage Assessment Procedures
There are five key procedures in the process flow of the damage estimation framework proposed in
this study, as shown in Figure 1. Firstly, according to Section 2.2, the method of POA compensation is
performed using the PolSAR data after preprocessing, and the new [T3] matrix after POA compensation
is obtained.
Figure 1. The process flow diagram of the damage estimation framework. “YFCD”, “BB”, “Re” and
“Im” represent the Yamaguchi four-component decomposition, the buildings divergent to satellite
flight pass, and the real part and imaginary part of the complex number, respectively. ε, ε1, and ε2
are the threshold values.
Secondly, the Wishart supervised classification is performed on the PolSAR data after POA
compensation, and this procedure classifies the ground objects into the four classes of undamaged
buildings, collapsed buildings, vegetation, and bare areas.
Thirdly, Yamaguchi four-component decomposition is performed on the PolSAR data before and
after POA compensation, respectively. At the same time, the two dihedral components are respectively
extracted to compute the NDDC, as described in Section 2.4. Next, the ρHHHV parameter described in
Section 2.4 is computed. The buildings divergent to satellite flight pass are then extracted using the
two parameters of the NDDC and ρHHHV. The data items of buildings divergent to satellite flight pass
are added to the undamaged buildings generated from the Wishart supervised classification, and they
become the total undamaged buildings.
According to the third step, the undamaged buildings are extracted. The collapsed buildings
are extracted in the fourth step. The vegetation class generated from the Wishart supervised
classification is corrected by meeting the condition of NDDC < ε. The final output of the class of bare
areas is the same as the classification result of the Wishart supervised classification. After the three
Original PolSAR data
Preprocessing
[S2] matrix [T3] matrix
POA shift matrix
POA compensation
[T3] matrix
Wishart supervised classification
Bare
areas
Collapsed
buildings
Undamaged
buildings Vegetation
Dihedral
component
Dihedral
component
NDDC>ε
Re( HHHV)< 1&&
Im( HHHV)< 2
BB
NDDC
HHHV
True
False
False
Undamaged
buildings
The remaining data items
are the collapsed buildings
Bare
areas Vegetation
YFCD
YFCD
The results of the earthquake
damage assessment
BBCR
Figure 1.
The process flow diagram of the damage estimation framework. “YFCD”, “BB”, “Re” and
“Im” represent the Yamaguchi four-component decomposition, the buildings divergent to satellite
flight pass, and the real part and imaginary part of the complex number, respectively.
ε
,
ε
1, and
ε
2 are
the threshold values.
Secondly, the Wishart supervised classification is performed on the PolSAR data after POA
compensation, and this procedure classifies the ground objects into the four classes of undamaged
buildings, collapsed buildings, vegetation, and bare areas.
Thirdly, Yamaguchi four-component decomposition is performed on the PolSAR data before and
after POA compensation, respectively. At the same time, the two dihedral components are respectively
extracted to compute the NDDC, as described in Section 2.4. Next, the
ρHHHV
parameter described in
Section 2.4 is computed. The buildings divergent to satellite flight pass are then extracted using the
two parameters of the NDDC and
ρHHHV
. The data items of buildings divergent to satellite flight pass
are added to the undamaged buildings generated from the Wishart supervised classification, and they
become the total undamaged buildings.
According to the third step, the undamaged buildings are extracted. The collapsed buildings are
extracted in the fourth step. The vegetation class generated from the Wishart supervised classification

Remote Sens. 2016,8, 171 4 of 14
is corrected by meeting the condition of NDDC <
ε
. The final output of the class of bare areas is the
same as the classification result of the Wishart supervised classification. After the three classes of
undamaged buildings, vegetation, and bare areas are determined, the remaining data items are the
collapsed buildings.
Finally, the building collapse rate of each block is derived from the BBCR index described in
Section 2.6, and the damage levels of all the blocks are divided into three levels according to the
threshold values of the BBCR index. The earthquake damage assessment with three damage levels
is then mapped out for the whole urban region. The methodology and parameters are introduced in
detail in the next section.
2.2. Polarization Orientation Angle (POA) Compensation
For enhancing the contrast between buildings divergent to satellite flight pass and collapsed
buildings, the scheme of POA compensation can be used to increase the double-bounce scattering
power of buildings divergent to satellite flight pass.
The buildings divergent to satellite flight pass patch can induce the POA shift
θ
, which can be
estimated by Equation (1) based on the circular polarization method [21,22]:
θ“$
&
%
θ0,i f θ0ďπ
4
θ0´π
2,i f θ0ąπ
4
(1)
where
θ0“1
4rArg pxSR RS˚
LL yq ` πs(2)
According to Lee [21], the data compensation of the orientation angle of θcan be achieved by:
Tθ“RpθqTR pθqT(3)
where the superscript Tdenotes the matrix transpose, and the rotation matrix R(θ)is given by:
Rpθq “ »
—
–
1 0 0
0 cos2θsin2θ
0´sin2θcos2θ
fi
ffi
fl(4)
2.3. Wishart Supervised Classification
The scattering power of PolSAR imagery can be greatly increased using the method of POA
compensation. Therefore, in order to extract the undamaged buildings as completely as possible,
Wishart supervised classification based on the complex Wishart distribution of the polarimetric
coherency matrix [
23
] is performed on the PolSAR data after POA compensation. The classification
algorithm proposed in [
24
] for polarimetric SAR images is the recommended method for supervised
classification. Details of the Wishart supervised classification algorithm can be found in [
23
].
The ground objects are classified into four categories using the Wishart supervised classification:
undamaged buildings, collapsed buildings, vegetation, and bare areas. Among the classification
results, the buildings divergent to satellite flight pass will be mixed in both the collapsed buildings
and vegetation classes, so the two classes obtained from the Wishart supervised classification will be
inaccurate. The undamaged buildings class still lack some of the buildings divergent to satellite flight
pass whose scattering power is not strong enough. However, the bare areas class has a high reliability.
Therefore, the results of the Wishart supervised classification are considered as the initial classification
results for extracting the building damage information. The initial extraction results are then improved
using the following two indicators.

Remote Sens. 2016,8, 171 5 of 14
2.4. Building Divergent to Satellite Flight Extraction
Because the results of the Wishart supervised classification are not very accurate, the
two parameters of the NDDC and
ρHHHV
are proposed to correct the initial classification results.
The buildings divergent to satellite flight pass are extracted using the two parameters of the NDDC
and ρHHHV, which are introduced in the following.
The indicator of the difference of the dihedral component (DDC) is defined as the difference
between the dihedral component obtained from the Yamaguchi four-component decomposition [
25
,
26
]
before and after POA compensation. It can be expressed as: the DDC equals the dihedral component
after POA compensation minus the dihedral component before POA compensation. The DDC is
normalized to a positive value range, which is named the normalized difference of the dihedral
component, or the NDDC for short.
The scattering power of the buildings divergent to satellite flight pass is greatly increased after the
POA compensation. Meanwhile, the scattering intensity of the dihedral component generated from
the Yamaguchi four-component decomposition is also increased. The NDDC can measure the change
in the double-bounce scattering power after the POA compensation. The double-bounce scattering
power of the buildings divergent to satellite flight pass changes a great deal after the POA compensation,
while that of the targets with reflection symmetry changes little. That is, the NDDC values of the
buildings divergent to satellite flight pass are high and those of the targets with reflection symmetry are
low. Hence, the NDDC can be introduced in the process of earthquake damage assessment to find the
buildings divergent to satellite flight pass, which can be used to correct the classification result of the
undamaged buildings generated from the Wishart supervised classification. The scattering intensity of
some buildings divergent to satellite flight pass is not increased to as strong as the buildings parallel to
satellite flight pass using the method of POA compensation. Therefore, the class of undamaged buildings
obtained from the Wishart supervised classification is not complete. The main missing undamaged
buildings are the buildings divergent to satellite flight pass whose scattering intensity is not significantly
increased. The data items with high NDDC values correspond to the buildings divergent to satellite
flight pass, which can be added to the undamaged buildings. The threshold value
ε
of the NDDC is set to
distinguish the buildings divergent to satellite flight pass from the other ground objects:
xPbuildings divergent to satellite f light pass,i f NDDC pxq ą ε(5)
The ρHHHV parameter is computed using the following equation:
ρHH HV “xSHHSHV
˚y
axSHH SHH
˚yaxSHV SHV
˚y(6)
where the superscript * denotes the complex conjugate.
In our experiments, we found that the distributions in the complex plane of the
ρHHHV
parameter
for the buildings divergent to satellite flight pass and collapsed buildings are different. The
ρHHHV
parameter for the collapsed buildings is mainly distributed in the third quadrant of the complex plane,
while the
ρHHHV
parameter for the buildings divergent to satellite flight pass is mainly distributed in
the other areas of the complex plane. Therefore, the buildings divergent to satellite flight pass can be
extracted using the ρHHHV parameter by the expression:
xPbuildings divergent to satellite f light pass,
i f pRe pρHHHV pxqq ă ε1& &Im pρHH HV pxqq ă ε2q “ 0(7)
where xis the data sample of the PolSAR imagery; “Re” and “Im” denote the real part and imaginary
part of the complex number, respectively; and
ε1
and
ε2
are the two threshold values of the real part
and imaginary part of ρHHHV, respectively.

Remote Sens. 2016,8, 171 6 of 14
The
ρHHHV
parameter is computed using the PolSAR data without POA compensation, which
can better distinguish collapsed buildings from buildings divergent to satellite flight pass than using
the PolSAR data after POA compensation, based on experiments.
In this work, the parameters of
ρHHHV
and the NDDC together determine the buildings divergent
to satellite flight pass. The buildings divergent to satellite flight pass extracted using the NDDC include
some collapsed buildings with the walls divergent to satellite flight, but the buildings divergent to
satellite flight pass extracted by the
ρHHHV
parameter contain a few collapsed buildings with the walls
divergent to satellite flight. Therefore, the data items simultaneously satisfying the two conditions of
ρHHHV
and the NDDC are determined as the buildings divergent to satellite flight pass, which can result
in a higher extraction accuracy for the buildings divergent to satellite flight pass. Using the parameters
of ρHHHV and the NDDC to extract the buildings divergent to satellite flight pass can be expressed as:
xPbuildings divergent to satellite f light pass,
i f NDDC pxq ą ε&
pRe pρHH HV pxqq ă ε1& &Im pρHHHV pxqq ă ε2q “ 0
(8)
The buildings divergent to satellite flight pass extracted by the parameters of
ρHHHV
and the
NDDC, together with the undamaged buildings generated from the Wishart supervised classification,
are the final output undamaged buildings.
2.5. Collapsed Building Extraction
The vegetation class generated from the Wishart supervised classification still needs to be corrected.
The NDDC values of vegetation items with reflection symmetry are low, and this property can be
used to correct the classification result of the Wishart supervised classification for the vegetation class.
Therefore, the data items of the vegetation generated from the Wishart supervised classification should
simultaneously satisfy the condition of NDDC <
ε
, and can be determined as the final output of the
vegetation class. The buildings divergent to satellite flight pass are determined according to Section 2.4,
and the bare areas are obtained through the Wishart supervised classification. By excluding the data
items of the above three classes, the remaining data items can be classified as the collapsed buildings.
2.6. Building Collapse Rate Calculation
The building collapse rate is calculated at the block scale, and is defined as the ratio of the
collapsed building samples to the total number of building samples in one block. The damage level
of one block can be indexed by the building collapse rate of the block. The building collapse rate of
blocks is introduced to handle the damage assessment at the block scale, and it is termed the building
block collapse rate (BBCR). The blocks separated by roads are regarded as individual areas of similar
built-up patch structure [
19
]. Each block is assigned a BBCR to assess the damage level of the block.
The BBCR is expressed as:
BBCRj“ř
i
Cij
ř
i
Uij `ř
i
Cij
(9)
where BBCR
j
is the BBCR of the jth block; C
ij
indicates whether pixel iin the jth block belongs to
a collapsed building or not, with values of 0 or 1; and U
ij
indicates whether pixel iin the jth block
belongs to a undamaged building or not, with values of 0 or 1.
3. Experimental Results and Analysis
3.1. Experimental Data
The study case is the “4.14” Yushu earthquake with magnitude 7.1 which occurred on
14th April, 2010.
This earthquake severely affected the county of Yushu in Qinghai province of China. The location of the

Remote Sens. 2016,8, 171 7 of 14
epicenter was 33.1
˝
N and 96.6
˝
E, as shown in Figure 2. The area is characterized by complicated terrain,
poor transport infrastructure, and harsh climate. The vegetation of the Yushu urban region is sparse
and low level, and the buildings are mainly rural residential buildings. More than 240,000 people were
affected by the earthquake, and more than 2600 people died as a result of the earthquake. There were
many collapsed buildings and more than 22 billion CNY of direct economic losses. The experiments
were carried out on the post-event airborne PolSAR imagery to validate the effectiveness of the
proposed approach for earthquake damage assessment. The PolSAR imagery was acquired one
day after the earthquake by the Chinese airborne SAR mapping system (SARMapper), which was
developed by a group led by the Chinese Academy of Surveying and Mapping (CASM). The system
collects P-band data, and both the range resolution and azimuth resolution are approximately 1 m.
Some specific information about the PolSAR data used in this work is listed in Table 1. The Pauli RGB
image is shown in Figure 3, formed as a color composite of |HH
´
VV| (red), |HV| (green), and
|HH + VV| (blue), with the size of 8192
ˆ
4384 pixels. The mountains surrounding Yushu County
account for a large part of the imagery. In order to focus on analyzing the buildings of the urban
area, an urban area mask was applied to discard the mountains and preserve the urban area as the
region of interest. To allow a comparison with the existing damage assessment maps of the 4.14 Yushu
earthquake [
19
,
27
,
28
], the geographic information system (GIS) data layer was depicted manually
along the major roads and by reference to these damage assessment maps. The urban area was divided
into 72 blocks by the GIS data layer containing 72 polygons, and the layout and characteristics of the
buildings within the city blocks were similar. The ground-truth map shown in Figure 4was drawn
with reference to the above-mentioned damage assessment maps [
19
,
27
,
28
] and the high-resolution
QuickBird image of Yushu County, and the damage levels were grouped into three levels for the
72 city blocks.
Table 1.
Information about the fully-polarimetric synthetic aperture radar (PolSAR) data used in this work.
Date Flight Direction Illumination
Direction
Incidence
Angle Band Flight
Altitude (m)
Spatial
Resolution (m)
15 April 2010
From right to left Bottom 50˝P 10,079 1 (range);
1 (azimuth)
O
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
50°N
50°N
40°N
40°N
30°N
30°N
20°N
20°N
±
0 990,000 1,980,000 Meters
"4.14" Yushu Earthquake
Ms7.1
Date: 14 April,2010
Depth: 33km
Epicenter: 33.1°N, 96.6°E
Yushu County
Qinghai
Figure 2. Map of the location of Yushu earthquake.

Remote Sens. 2016,8, 171 8 of 14
Remote Sens. 2016, 8, 171 8 of 14
Figure 3. Pauli RGB color composite (|HH − VV| (red), |HV| (green), and |HH + VV| (blue)) image
of Yushu County. The areas marked by red and blue rectangles are the samples for collapsed
buildings and buildings divergent to satellite flight pass, respectively. The center coordinates of the
image are 33°0′9′′N and 97°0′11′′E.
Figure 4. Reference map for the earthquake damage assessment, with three damage levels: slight
damage, moderate damage, and serious damage. If more than half of the buildings collapsed after
the earthquake, the city block was considered as serious damage. The city block with less than
one-third buildings collapsing was considered as slight damage. The city block with the damage
level between slight damage and serious damage was considered as moderate damage.
3.2. The Results of the Experiments
According to the process flow of the earthquake damage assessment shown in Figure 1, the POA
shift was first estimated based on the circular polarization method, and the POA compensation
described in Section 2.2 was carried out for the PolSAR data after speckle noise filtering. The two
dihedral components before and after POA compensation were then extracted on the basis of
Yamaguchi four-component decomposition. As can be seen from Figure 5, the dihedral component
image brightness changed after the POA compensation, and there are great differences in some parts
of the two images, such as the upper right part and the bottom left part. These areas are mainly
undamaged buildings divergent to satellite flight pass and collapsed buildings divergent to satellite
flight pass. The NDDC parameter described in Section 2.4 was calculated and is shown in Figure 6.
According to the color bar in Figure 6, 190 can be easily chosen as the threshold value for
distinguishing the buildings divergent to satellite flight pass from the other ground objects, which are
mainly reflection symmetric. The areas corresponding to NDDC > 190 are mainly buildings divergent
to satellite flight pass. Meanwhile, the PolSAR data after POA compensation were classified into
undamaged buildings, collapsed buildings, vegetation, and bare areas using the Wishart supervised
classification described in Section 2.3. For vegetation, the NDDC was less than 190. Therefore, if a data
Figure 3.
Pauli RGB color composite (|HH
´
VV| (red), |HV| (green), and |HH + VV| (blue)) image
of Yushu County. The areas marked by red and blue rectangles are the samples for collapsed buildings
and buildings divergent to satellite flight pass, respectively. The center coordinates of the image are
33˝01911 N and 97˝01111 1 E.
Remote Sens. 2016, 8, 171 8 of 14
Figure 3. Pauli RGB color composite (|HH − VV| (red), |HV| (green), and |HH + VV| (blue)) image
of Yushu County. The areas marked by red and blue rectangles are the samples for collapsed
buildings and buildings divergent to satellite flight pass, respectively. The center coordinates of the
image are 33°0′9′′N and 97°0′11′′E.
Figure 4. Reference map for the earthquake damage assessment, with three damage levels: slight
damage, moderate damage, and serious damage. If more than half of the buildings collapsed after
the earthquake, the city block was considered as serious damage. The city block with less than
one-third buildings collapsing was considered as slight damage. The city block with the damage
level between slight damage and serious damage was considered as moderate damage.
3.2. The Results of the Experiments
According to the process flow of the earthquake damage assessment shown in Figure 1, the POA
shift was first estimated based on the circular polarization method, and the POA compensation
described in Section 2.2 was carried out for the PolSAR data after speckle noise filtering. The two
dihedral components before and after POA compensation were then extracted on the basis of
Yamaguchi four-component decomposition. As can be seen from Figure 5, the dihedral component
image brightness changed after the POA compensation, and there are great differences in some parts
of the two images, such as the upper right part and the bottom left part. These areas are mainly
undamaged buildings divergent to satellite flight pass and collapsed buildings divergent to satellite
flight pass. The NDDC parameter described in Section 2.4 was calculated and is shown in Figure 6.
According to the color bar in Figure 6, 190 can be easily chosen as the threshold value for
distinguishing the buildings divergent to satellite flight pass from the other ground objects, which are
mainly reflection symmetric. The areas corresponding to NDDC > 190 are mainly buildings divergent
to satellite flight pass. Meanwhile, the PolSAR data after POA compensation were classified into
undamaged buildings, collapsed buildings, vegetation, and bare areas using the Wishart supervised
classification described in Section 2.3. For vegetation, the NDDC was less than 190. Therefore, if a data
Figure 4.
Reference map for the earthquake damage assessment, with three damage levels: slight
damage, moderate damage, and serious damage. If more than half of the buildings collapsed after
the earthquake, the city block was considered as serious damage. The city block with less than
one-third buildings collapsing was considered as slight damage. The city block with the damage level
between slight damage and serious damage was considered as moderate damage.
3.2. The Results of the Experiments
According to the process flow of the earthquake damage assessment shown in Figure 1, the POA
shift was first estimated based on the circular polarization method, and the POA compensation described
in Section 2.2 was carried out for the PolSAR data after speckle noise filtering. The two dihedral
components before and after POA compensation were then extracted on the basis of Yamaguchi
four-componentdecomposition. As can be seen from Figure 5, the dihedral component image brightness
changed after the POA compensation, and there are great differences in some parts of the two images,
such as the upper right part and the bottom left part. These areas are mainly undamaged buildings
divergent to satellite flight pass and collapsed buildings divergent to satellite flight pass. The NDDC
parameter described in Section 2.4 was calculated and is shown in Figure 6. According to the color
bar in Figure 6, 190 can be easily chosen as the threshold value for distinguishing the buildings
divergent to satellite flight pass from the other ground objects, which are mainly reflection symmetric.
The areas corresponding to NDDC > 190 are mainly buildings divergent to satellite flight pass.
Meanwhile, the PolSAR data after POA compensation were classified into undamaged buildings,
collapsed buildings, vegetation, and bare areas using the Wishart supervised classification described

Remote Sens. 2016,8, 171 9 of 14
in Section 2.3. For vegetation, the NDDC was less than 190. Therefore, if a data item was classified as
vegetation by the Wishart supervised classification, and simultaneously satisfied NDDC < 190, it was
finally classified as vegetation.
Remote Sens. 2016, 8, 171 9 of 14
item was classified as vegetation by the Wishart supervised classification, and simultaneously satisfied
NDDC < 190, it was finally classified as vegetation.
(a)
(b)
Figure 5. Images of the dihedral component before and after POA compensation. (a) before POA
compensation; (b) after POA compensation.
Figure 6. NDDC map.
The ρHHHV parameter was calculated according to Section 2.4 using the PolSAR data without
POA compensation. The 5000 samples were randomly selected from the two regions of interest
(ROIs) of buildings divergent to satellite flight pass and the two ROIs of collapsed buildings,
respectively, and are shown in Figure 7. As can be seen from Figure 7, the collapsed building
samples are mainly distributed in the third quadrant of the complex plane, and the buildings
divergent to satellite flight samples are mainly located in other parts of the complex plane.
Figure 5.
Images of the dihedral component before and after POA compensation. (
a
) before POA
compensation; (b) after POA compensation.
Remote Sens. 2016, 8, 171 9 of 14
item was classified as vegetation by the Wishart supervised classification, and simultaneously satisfied
NDDC < 190, it was finally classified as vegetation.
(a)
(b)
Figure 5. Images of the dihedral component before and after POA compensation. (a) before POA
compensation; (b) after POA compensation.
Figure 6. NDDC map.
The ρHHHV parameter was calculated according to Section 2.4 using the PolSAR data without
POA compensation. The 5000 samples were randomly selected from the two regions of interest
(ROIs) of buildings divergent to satellite flight pass and the two ROIs of collapsed buildings,
respectively, and are shown in Figure 7. As can be seen from Figure 7, the collapsed building
samples are mainly distributed in the third quadrant of the complex plane, and the buildings
divergent to satellite flight samples are mainly located in other parts of the complex plane.
Figure 6. NDDC map.
The
ρHHHV
parameter was calculated according to Section 2.4 using the PolSAR data without
POA compensation. The 5000 samples were randomly selected from the two regions of interest (ROIs)
of buildings divergent to satellite flight pass and the two ROIs of collapsed buildings, respectively,
and are shown in Figure 7. As can be seen from Figure 7, the collapsed building samples are mainly
distributed in the third quadrant of the complex plane, and the buildings divergent to satellite flight
samples are mainly located in other parts of the complex plane. Therefore, the buildings divergent to

Remote Sens. 2016,8, 171 10 of 14
satellite flight pass class can be finally determined by the condition that the complex number
ρHHHV
is not in the third quadrant of the complex plane, and the NDDC is greater than 190. The buildings
divergent to satellite flight pass and the undamaged buildings generated from the Wishart supervised
classification, which are mainly undamaged buildings parallel to satellite flight pass, together form the
final classification result of the undamaged buildings.
Remote Sens. 2016, 8, 171 10 of 14
Therefore, the buildings divergent to satellite flight pass class can be finally determined by the
condition that the complex number ρHHHV is not in the third quadrant of the complex plane, and the
NDDC is greater than 190. The buildings divergent to satellite flight pass and the undamaged
buildings generated from the Wishart supervised classification, which are mainly undamaged
buildings parallel to satellite flight pass, together form the final classification result of the
undamaged buildings.
Figure 7. ρHHHV of the building divergent to satellite flight samples and collapsed building samples.
The red dots and blue dots represent the collapsed building samples and the building divergent to
satellite flight samples, respectively.
The bare areas class was determined by the classification result of the Wishart supervised
classification. The collapsed buildings were considered to be the remaining parts after removing the
three classes of vegetation, undamaged buildings, and bare areas. The distribution map of the
non-buildings and the three kinds of buildings is shown in Figure 8. The building damage level index
of each block was implemented using the BBCR method described in Section 2.6, as shown in Figure 9.
A greater BBCR value corresponds to a more seriously damaged block. Three damage levels were set
for the building collapse degree of the blocks. When the BBCR was less than 0.3, the damage level was
set as slight damage. A BBCR of greater than 0.5 was set as the serious damage level, and a BBCR value
of between 0.3 and 0.5 was considered to be moderate damage. The results of the damage assessment
are shown in Figure 10, where the numbered blocks are the misclassified blocks, and the color of the
numbers denotes the correct damage level. For example, the no. 8 block should actually be slightly
damaged (green), but is misclassified as moderate damage (blue).
Figure 8. Distribution map of the non-buildings and the three kinds of buildings at the block scale.
The NB, PB, BB and CB represent the non-buildings, the buildings parallel to satellite flight pass, the
buildings divergent to satellite flight pass and the collapsed buildings, respectively.
Figure 7. ρHHHV
of the building divergent to satellite flight samples and collapsed building samples.
The red dots and blue dots represent the collapsed building samples and the building divergent to
satellite flight samples, respectively.
The bare areas class was determined by the classification result of the Wishart supervised
classification. The collapsed buildings were considered to be the remaining parts after removing
the three classes of vegetation, undamaged buildings, and bare areas. The distribution map of the
non-buildings and the three kinds of buildings is shown in Figure 8. The building damage level index
of each block was implemented using the BBCR method described in Section 2.6, as shown in Figure 9.
A greater BBCR value corresponds to a more seriously damaged block. Three damage levels were set
for the building collapse degree of the blocks. When the BBCR was less than 0.3, the damage level was
set as slight damage. A BBCR of greater than 0.5 was set as the serious damage level, and a BBCR value
of between 0.3 and 0.5 was considered to be moderate damage. The results of the damage assessment
are shown in Figure 10, where the numbered blocks are the misclassified blocks, and the color of the
numbers denotes the correct damage level. For example, the no. 8 block should actually be slightly
damaged (green), but is misclassified as moderate damage (blue).
Remote Sens. 2016, 8, 171 10 of 14
Therefore, the buildings divergent to satellite flight pass class can be finally determined by the
condition that the complex number ρHHHV is not in the third quadrant of the complex plane, and the
NDDC is greater than 190. The buildings divergent to satellite flight pass and the undamaged
buildings generated from the Wishart supervised classification, which are mainly undamaged
buildings parallel to satellite flight pass, together form the final classification result of the
undamaged buildings.
Figure 7. ρHHHV of the building divergent to satellite flight samples and collapsed building samples.
The red dots and blue dots represent the collapsed building samples and the building divergent to
satellite flight samples, respectively.
The bare areas class was determined by the classification result of the Wishart supervised
classification. The collapsed buildings were considered to be the remaining parts after removing the
three classes of vegetation, undamaged buildings, and bare areas. The distribution map of the
non-buildings and the three kinds of buildings is shown in Figure 8. The building damage level index
of each block was implemented using the BBCR method described in Section 2.6, as shown in Figure 9.
A greater BBCR value corresponds to a more seriously damaged block. Three damage levels were set
for the building collapse degree of the blocks. When the BBCR was less than 0.3, the damage level was
set as slight damage. A BBCR of greater than 0.5 was set as the serious damage level, and a BBCR value
of between 0.3 and 0.5 was considered to be moderate damage. The results of the damage assessment
are shown in Figure 10, where the numbered blocks are the misclassified blocks, and the color of the
numbers denotes the correct damage level. For example, the no. 8 block should actually be slightly
damaged (green), but is misclassified as moderate damage (blue).
Figure 8. Distribution map of the non-buildings and the three kinds of buildings at the block scale.
The NB, PB, BB and CB represent the non-buildings, the buildings parallel to satellite flight pass, the
buildings divergent to satellite flight pass and the collapsed buildings, respectively.
Figure 8.
Distribution map of the non-buildings and the three kinds of buildings at the block scale.
The NB, PB, BB and CB represent the non-buildings, the buildings parallel to satellite flight pass, the
buildings divergent to satellite flight pass and the collapsed buildings, respectively.

Remote Sens. 2016,8, 171 11 of 14
Remote Sens. 2016, 8, 171 11 of 14
Figure 9. BBCR map for each block.
Figure 10. The damage assessment results. The numbered blocks are misclassified, and the color of
the numbers denotes the color of the correct damage level.
3.3. Analysis and Discussion
The whole procedure of the production of a damage assessment map has taken five to six hours,
which can meet rapid acquisition for buildings damage information after an earthquake. The
damage assessment accuracies of the proposed method and the method only using the PolSAR data
without POA compensation for the Wishart supervised classification are listed in Table 2. As can be
seen in Table 2, the method proposed in this study significantly improves the damage assessment
accuracy. In addition, compared with the method proposed by Zhao et al. [19], the proposed method
again greatly improves the damage assessment accuracy. For the seriously damaged blocks, there
are actually 25 blocks in the ground-truth map, but there are 31 blocks classified by the Wishart
supervised classification using the PolSAR data without POA compensation, while there are 26 blocks
generated by the proposed method. Therefore, the method proposed in this study also reduces the
over-assessment of the building collapse rate, which could help to save human, physical, and
financial resources for the emergency rescue after an earthquake.
Table 2. Comparison of the damage assessment accuracy of the two methods.
The Pro
p
osed Method Wishart Su
p
ervised Classification
(Ex
p
eriment)
SLD MOD SED SLD MOD SED
(No. of Blocks)
(Reference)
SLD 10 4 0 9 3 2
MOD 1 30 2 3 22 8
SED 0 1 24 0 4 21
OA: 88.89% OA: 72.22%
OA, SLD, MOD, and SED represent overall accuracy, slight damage, moderate damage, and serious damage,
respectively.
Figure 9. BBCR map for each block.
Remote Sens. 2016, 8, 171 11 of 14
Figure 9. BBCR map for each block.
Figure 10. The damage assessment results. The numbered blocks are misclassified, and the color of
the numbers denotes the color of the correct damage level.
3.3. Analysis and Discussion
The whole procedure of the production of a damage assessment map has taken five to six hours,
which can meet rapid acquisition for buildings damage information after an earthquake. The
damage assessment accuracies of the proposed method and the method only using the PolSAR data
without POA compensation for the Wishart supervised classification are listed in Table 2. As can be
seen in Table 2, the method proposed in this study significantly improves the damage assessment
accuracy. In addition, compared with the method proposed by Zhao et al. [19], the proposed method
again greatly improves the damage assessment accuracy. For the seriously damaged blocks, there
are actually 25 blocks in the ground-truth map, but there are 31 blocks classified by the Wishart
supervised classification using the PolSAR data without POA compensation, while there are 26 blocks
generated by the proposed method. Therefore, the method proposed in this study also reduces the
over-assessment of the building collapse rate, which could help to save human, physical, and
financial resources for the emergency rescue after an earthquake.
Table 2. Comparison of the damage assessment accuracy of the two methods.
The Pro
p
osed Method Wishart Su
p
ervised Classification
(Ex
p
eriment)
SLD MOD SED SLD MOD SED
(No. of Blocks)
(Reference)
SLD 10 4 0 9 3 2
MOD 1 30 2 3 22 8
SED 0 1 24 0 4 21
OA: 88.89% OA: 72.22%
OA, SLD, MOD, and SED represent overall accuracy, slight damage, moderate damage, and serious damage,
respectively.
Figure 10.
The damage assessment results. The numbered blocks are misclassified, and the color of the
numbers denotes the color of the correct damage level.
3.3. Analysis and Discussion
The whole procedure of the production of a damage assessment map has taken five to six hours,
which can meet rapid acquisition for buildings damage information after an earthquake. The damage
assessment accuracies of the proposed method and the method only using the PolSAR data without
POA compensation for the Wishart supervised classification are listed in Table 2. As can be seen in
Table 2, the method proposed in this study significantly improves the damage assessment accuracy.
In addition, compared with the method proposed by Zhao et al. [
19
], the proposed method again
greatly improves the damage assessment accuracy. For the seriously damaged blocks, there are
actually 25 blocks in the ground-truth map, but there are 31 blocks classified by the Wishart supervised
classification using the PolSAR data without POA compensation, while there are 26 blocks generated by
the proposed method. Therefore, the method proposed in this study also reduces the over-assessment
of the building collapse rate, which could help to save human, physical, and financial resources for the
emergency rescue after an earthquake.
Table 2. Comparison of the damage assessment accuracy of the two methods.
The Proposed Method Wishart Supervised Classification
(Experiment)
SLD MOD SED SLD MOD SED
(No. of Blocks)
(Reference)
SLD 10 4 0 9 3 2
MOD 1 30 2 3 22 8
SED 0 1 24 0 4 21
OA: 88.89% OA: 72.22%
OA, SLD, MOD, and SED represent overall accuracy, slight damage, moderate damage, and serious
damage, respectively.

Remote Sens. 2016,8, 171 12 of 14
For the results of the method proposed in this study with the Yushu earthquake data, there are
eight blocks misclassified. Among them, the damage levels of both block no. 1 and block no. 4 are
underestimated, while the damage levels of blocks no. 2 and no. 3 and blocks nos. 5 to 8 are overestimated.
These blocks are mainly low-rise and small rural residential buildings with earth/wood structure
or masonry structure. The remaining walls of the collapsed buildings present dihedral scattering
characteristics and are easily misclassified as undamaged buildings. Thus, some remaining parts
of the damaged buildings parallel to satellite flight pass and the damaged buildings divergent to
satellite flight pass may be misclassified as undamaged buildings, which is the main reason for the
underestimation of the block damage level. The buildings divergent to satellite flight pass cannot be
completely extracted with just the two parameters of the NDDC and ρHHHV, so some ground objects
with comparatively weak scattering power and characterized by volume scattering are still classified as
collapsed buildings. This results in the overestimation of the building collapse rate at the block scale.
4. Conclusions
Collapsed buildings caused by an earthquake are one of the main causes of casualties, so rapid
acquisition of the collapsed building information after the earthquake can play an extremely important
role in saving lives. The use of only a single post-earthquake PolSAR image to extract the collapse
information can meet the needs of rapid and accurate disaster information acquisition, and can assist
with a rapid and effective emergency rescue operation. In this study of earthquake damage assessment,
the two parameters of the NDDC and
ρHHHV
were used to improve the classification results of Wishart
supervised classification performed on the PolSAR data after POA compensation, with the aim of
obtaining non-buildings, undamaged buildings (including the buildings parallel to satellite flight pass
and the buildings divergent to satellite flight pass), and collapsed buildings.
This feasibility study was performed on the airborne PolSAR imagery acquired one day after
the Yushu earthquake. The buildings divergent to satellite flight pass were extracted based on the
conditions of NDDC > 190 and
ρHHHV
not being in the third quadrant of the complex plane, and were
included in the undamaged buildings. These operations improved the accuracy of the undamaged
building extraction. Using the condition of NDDC < 190 to restrict the vegetation generated from
the Wishart supervised classification improved the non-building extraction accuracy and reduced the
numbers of collapsed buildings divergent to satellite flight pass mixed with the vegetation class. At the
same time, the above operations excluded the non-buildings and undamaged buildings as much as
possible, and also improved the extraction accuracy of the collapsed buildings. Finally, the earthquake
damage assessment map of the Yushu urban region with three damage levels at the block scale was
obtained according to the value of the BBCR index for each block. The damage assessment at the block
scale can not only be flexibly applied to multiple-resolution radar images and can avoid some of the
errors of damage assessment at the single-building scale, but could also be more effective in assisting
with making comprehensive arrangements in the process of emergency rescue.
All in all, the method proposed in this study can greatly improve the accuracy of earthquake
damage assessment. Nevertheless, some undamaged buildings divergent to satellite flight still cannot
be extracted, and some remaining parts of collapsed buildings are easily misidentified as undamaged
buildings, which is the main reason for the errors in the building earthquake damage information
extraction. In our future work, state-of-the-art filtering methods [
29
] and multi-classifier fusion [
30
,
31
]
will be considered to improve the current results.
Acknowledgments:
This work was supported by the Program for Changjiang Scholars and Innovative Research
Team in University (IRT1278); the Hundred Talent Program of the Chinese Academy of Sciences (29Y127D01);
the Cross-Disciplinary Collaborative Teams Program for Science, Technology and Innovation of the Chinese
Academy of Sciences; the Earthquake Science and Technology Development Fund Program of Lanzhou Earthquake
Research Institute, the China Earthquake Administration (2015M02); the Object-Oriented High Trusted SAR
Processing System of the National 863 Subject Program; and the Airborne Multiband Polarimetric Interferometric
SAR Mapping System of the National Major Surveying and Mapping Science and Technology Special Program.
We would also like to thank the anonymous reviewers for their advice on improving the quality of this paper.

Remote Sens. 2016,8, 171 13 of 14
Author Contributions:
Wei Zhai drafted the manuscript and was responsible for the research design, writing
the source code, data analysis, and interpretation of the results. HuanfengShen designed the structures of
the paper and guided the experiments. Chunlin Huang edited and reviewed the manuscript. Wansheng Pei
collected literatures.
Conflicts of Interest: The authors declare no conflict of interest.
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2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons by Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).